Data Platform Architect, Team Lead

Clasp•Boston, MA

16d•$155,000 - $190,000•Hybrid

About The Position

Data Platform Architect, Team Lead Location: Boston, MA (Hybrid — 2 days per week in office) About Us Clasp is a venture-backed, mission-driven startup transforming access to education and career pathways. We are revolutionizing the way employers attract and retain critical talent, while simultaneously tackling the student debt crisis. (Yep, we think BIG.) Our innovative platform meaningfully connects employers, educational institutions, and diverse talent to drive mutual benefit—using accessible education financing as the thread. We like to think of ourselves as more than a fintech; we’re a catalyst for economic mobility. A Forbes Fintech 50 company, portfolio company of SHRM (Society of Human Resource Management — the largest HR organization out there!) and recipient of “43 Start Ups to Bet Your Career On in 2025” by Business Insider, Clasp is driven by our commitment to social impact and innovation. We are reshaping the future of the workforce one opportunity at a time. Join us on our journey to give power to learners and unlock fulfilling careers that drive positive change in their communities and beyond. The Role – Data Platform Architect, Manager At Clasp , our full-stack Data team is responsible for building and operating the data backbone that connects our go-to-market systems, internal application systems, and our data warehouse. As we scale, we’re evolving the team’s structure: the core will focus on data orchestration, reliability, and interoperability, with some additional downstream reporting & analytics ownership as we scale the business. We’re seeking a strong leader to own the roadmap and execution of this transformation. You will lead the design, build-out, and operation of the data infrastructure that moves data reliably and consistently between systems, into our data warehouse, and out to analytics and consumer layers. You’ll lead a small pod, define SLAs, build observability, drive standards, and act as product owner for the “data as infrastructure” layer. You will partner closely with reporting, BI, and analytic teams to ensure data flows and semantic models are fit for purpose.

Requirements

5+ years of experience in data engineering / data infrastructure roles, of which 2+ years managing/leading other engineers or architects.
Proven track record designing and implementing data pipelines/infrastructure across multiple source systems into a data warehouse/lakehouse environment.
Expert experience with orchestration tooling (e.g., Airflow, Dagster), ingestion frameworks, cloud platforms (AWS/GCP/Azure), and data-warehouse technologies (Snowflake, BigQuery, Redshift, etc).
Expert experience with data modeling, semantic layers (dbt or equivalent), metadata/lineage tooling, data quality frameworks.
Strong knowledge of API design, data contracts, system interoperability, micro-services or event-driven data architectures is a plus.
Prior experience partnering with analytics/business teams (even if not owning dashboards) — able to translate needs into infrastructure requirements and deliver.
Excellent verbal and written communication skills; ability to present to senior leadership and cross-functional audiences.
Experience building or improving observability, monitoring, alerting for data pipelines.
Bachelor’s degree in computer science, engineering, mathematics or equivalent (or equivalent real-world experience).

Nice To Haves

Master’s or additional certifications are a plus.

Responsibilities

Own the architecture, roadmap, and operational health of the data-orchestration layer (ingestion, transformation, movement, observability) across internal systems, go-to-market systems (CRM, CS, etc.), and the data warehouse.
Lead a team of data engineers/ETL analysts on day-to-day project work
Define and enforce data pipelines' SLAs, error-recovery strategies, schema change management, metadata/lineage documentation.
Lead cross-system interoperability work: identify how data flows between source systems, warehouse, downstream consumers; design abstractions; reduce redundancy and latency.
Partner with reporting/analytics teams to ensure data models (via e.g., dbt) align with consumption needs, but shift ownership of reporting to those teams.
Own and evolve tooling around data quality, observability, lineage, and cataloging; propose infrastructure improvements.
Present to leadership on data-infrastructure health, trade-offs (speed vs. reliability), investment needs, and outcomes.
Responsible for P&L management for data infrastructure and tools
Evangelize “data as product/infrastructure” mindset across the organization: define APIs, data contracts, service level commitments to downstream analytics users.